{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Node classification with directed GraphSAGE\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/directed-graphsage-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/directed-graphsage-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example shows the application of *directed* GraphSAGE to a *directed* graph, where the in-node and out-node neighbourhoods are separately sampled and have different weights."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import stellargraph:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import DirectedGraphSAGENodeGenerator\n",
    "from stellargraph.layer import DirectedGraphSAGE\n",
    "\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the CORA network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, node_subjects = dataset.load(directed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "StellarDiGraph: Directed multigraph\n",
      " Nodes: 2708, Edges: 5429\n",
      "\n",
      " Node types:\n",
      "  paper: [2708]\n",
      "    Edge types: paper-cites->paper\n",
      "\n",
      " Edge types:\n",
      "    paper-cites->paper: [5429]\n"
     ]
    }
   ],
   "source": [
    "print(G.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We aim to train a graph-ML model that will predict the \"subject\" attribute on the nodes. These subjects are one of 7 categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Case_Based',\n",
       " 'Genetic_Algorithms',\n",
       " 'Neural_Networks',\n",
       " 'Probabilistic_Methods',\n",
       " 'Reinforcement_Learning',\n",
       " 'Rule_Learning',\n",
       " 'Theory'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(node_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for testing. We'll use scikit-learn again to do this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_subjects, test_subjects = model_selection.train_test_split(\n",
    "    node_subjects, train_size=0.1, test_size=None, stratify=node_subjects\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note using stratified sampling gives the following counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({'Probabilistic_Methods': 42,\n",
       "         'Genetic_Algorithms': 42,\n",
       "         'Neural_Networks': 81,\n",
       "         'Case_Based': 30,\n",
       "         'Theory': 35,\n",
       "         'Reinforcement_Learning': 22,\n",
       "         'Rule_Learning': 18})"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "\n",
    "Counter(train_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training set has class imbalance that might need to be compensated, e.g., via using a weighted cross-entropy loss in model training, with class weights inversely proportional to class support. However, we will ignore the class imbalance in this example, for simplicity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_subjects)\n",
    "test_targets = target_encoding.transform(test_subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now do the same for the node attributes we want to use to predict the subject. These are the feature vectors that the Keras model will use as input. The CORA dataset contains attributes 'w_x' that correspond to words found in that publication. If a word occurs more than once in a publication the relevant attribute will be set to one, otherwise it will be zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the GraphSAGE model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a data generator that feeds data from the graph to the model. The generators are specialized to the model and the learning task so we choose the `DirectedGraphSAGENodeGenerator` as we are predicting node attributes with a `DirectedGraphSAGE` model.\n",
    "\n",
    "We need two other parameters, the `batch_size` to use for training and the number of nodes to sample at each level of the model. Here we choose a two-level model with 10 nodes sampled in the first layer (5 in-nodes and 5 out-nodes), and 4 in the second layer (2 in-nodes and 2 out-nodes)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "in_samples = [5, 2]\n",
    "out_samples = [5, 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `DirectedGraphSAGENodeGenerator` object is required to send the node features in sampled subgraphs to Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = DirectedGraphSAGENodeGenerator(G, batch_size, in_samples, out_samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `generator.flow()` method, we can create iterators over nodes that should be used to train, validate, or evaluate the model. For training we use only the training nodes returned from our splitter and the target values. The `shuffle=True` argument is given to the `flow` method to improve training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_subjects.index, train_targets, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model, we need a few more parameters for this:\n",
    "\n",
    " * the `layer_sizes` is a list of hidden feature sizes of each layer in the model. In this example we use 32-dimensional hidden node features at each layer, which corresponds to 12 weights for each head node, 10 for each in-node and 10 for each out-node.\n",
    " * The `bias` and `dropout` are internal parameters of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "graphsage_model = DirectedGraphSAGE(\n",
    "    layer_sizes=[32, 32], generator=generator, bias=False, dropout=0.5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we create a model to predict the 7 categories using Keras softmax layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inp, x_out = graphsage_model.in_out_tensors()\n",
    "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the graph inputs `x_inp` provided by the `graph_model` and outputs being the predictions from the softmax layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model(inputs=x_inp, outputs=prediction)\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.005),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[\"acc\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the test set (we need to create another generator over the test data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_gen = generator.flow(test_subjects.index, test_targets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "6/6 - 3s - loss: 1.9108 - acc: 0.2037 - val_loss: 1.7470 - val_acc: 0.4208\n",
      "Epoch 2/20\n",
      "6/6 - 3s - loss: 1.6590 - acc: 0.4741 - val_loss: 1.6306 - val_acc: 0.5033\n",
      "Epoch 3/20\n",
      "6/6 - 3s - loss: 1.5334 - acc: 0.6407 - val_loss: 1.5296 - val_acc: 0.5747\n",
      "Epoch 4/20\n",
      "6/6 - 3s - loss: 1.4189 - acc: 0.7111 - val_loss: 1.4301 - val_acc: 0.6427\n",
      "Epoch 5/20\n",
      "6/6 - 3s - loss: 1.2873 - acc: 0.8222 - val_loss: 1.3533 - val_acc: 0.6887\n",
      "Epoch 6/20\n",
      "6/6 - 3s - loss: 1.1953 - acc: 0.8778 - val_loss: 1.2833 - val_acc: 0.6998\n",
      "Epoch 7/20\n",
      "6/6 - 3s - loss: 1.1191 - acc: 0.8704 - val_loss: 1.2165 - val_acc: 0.7125\n",
      "Epoch 8/20\n",
      "6/6 - 3s - loss: 1.0075 - acc: 0.9037 - val_loss: 1.1577 - val_acc: 0.7285\n",
      "Epoch 9/20\n",
      "6/6 - 3s - loss: 0.9367 - acc: 0.9333 - val_loss: 1.1151 - val_acc: 0.7301\n",
      "Epoch 10/20\n",
      "6/6 - 3s - loss: 0.8731 - acc: 0.9074 - val_loss: 1.0749 - val_acc: 0.7379\n",
      "Epoch 11/20\n",
      "6/6 - 4s - loss: 0.8125 - acc: 0.9519 - val_loss: 1.0341 - val_acc: 0.7465\n",
      "Epoch 12/20\n",
      "6/6 - 3s - loss: 0.7387 - acc: 0.9630 - val_loss: 0.9955 - val_acc: 0.7523\n",
      "Epoch 13/20\n",
      "6/6 - 3s - loss: 0.6990 - acc: 0.9519 - val_loss: 0.9530 - val_acc: 0.7596\n",
      "Epoch 14/20\n",
      "6/6 - 3s - loss: 0.6277 - acc: 0.9519 - val_loss: 0.9260 - val_acc: 0.7600\n",
      "Epoch 15/20\n",
      "6/6 - 3s - loss: 0.5893 - acc: 0.9704 - val_loss: 0.9063 - val_acc: 0.7683\n",
      "Epoch 16/20\n",
      "6/6 - 3s - loss: 0.5596 - acc: 0.9667 - val_loss: 0.8913 - val_acc: 0.7728\n",
      "Epoch 17/20\n",
      "6/6 - 3s - loss: 0.5042 - acc: 0.9889 - val_loss: 0.8663 - val_acc: 0.7678\n",
      "Epoch 18/20\n",
      "6/6 - 3s - loss: 0.4836 - acc: 0.9741 - val_loss: 0.8455 - val_acc: 0.7793\n",
      "Epoch 19/20\n",
      "6/6 - 3s - loss: 0.4544 - acc: 0.9778 - val_loss: 0.8271 - val_acc: 0.7789\n",
      "Epoch 20/20\n",
      "6/6 - 3s - loss: 0.4048 - acc: 0.9926 - val_loss: 0.8010 - val_acc: 0.7888\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen, epochs=20, validation_data=test_gen, verbose=2, shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LCyivcu5UDAsOYOKonvxybBo9u3Zy+/cSEWkLBbj4rOpaF09+9BMvLNjA0OQYnp00guTOLQ/WsOBAenbtdNgw3lNZw/bSCuIiQonpFNzWskVEPEIBLj5pW0kFN89cSubGnVx9Qi/uO2eQ2xb/iAwNIjJe93CLiH9RgIvPWZBVxK2zlrG3upa/TRzO+UN7eLskERGfowAXn+FyWZ6Zn81Tn62jb3wkz105gr4JWmRDRKQ5CnDxCTvKqrj93z/w5bpCLhzWg8cuHkynEP3PU0TkYPQvpHjdsk07uemNpRTtqeLRi47lilE9dc+1iMhhKMDFa6y1vPJNLo/OW0NidBjv3DhG912LiLSQAly8Yk9lDXe9s5wPl2/l9IEJ/PWyYbqFS0SkFRTg4nFrt+3mxjeWkFtUxl3jBzL15LR2ncdcRORooAAXj5qzNI97311BZGgwb/xyNCf06ertkkRE/JICXDyiorqWh/+zmje/38Txvbvw94nDtY62iEgbKMDF7TYVl3PjG0tYtaWUG0/tw51n9CdIS3KKiLSJAlzc6rPV27n9rR8wwAtXZzAuPdHbJYmIdAgKcHGbFXklTH19CYO6R/HcpJGkdNEKXyIi7UUBLm5RVePiN2//SNeIEN745WhiwnWLmIhIe1KAi1v848scftq2mxlXjVR4i4i4gUYSSbtbu203f/9fFucO6c6Zx3TzdjkiIh2SRwPcGDPeGLPWGJNtjLm7mdd7GmPmG2OWGWOWG2PO9mR90na1Lstv31lOVFgwD59/jLfLERHpsDwW4MaYQOBZ4CwgHZhojElv0ux+4C1r7XBgAjDdU/VJ+3hxwQZ+3LyLB89Lp2tkqLfLERHpsDzZAx8FZFtr11trq4BZwAVN2lgguu55DLDFg/VJG+UWlfHn/65l3KAEzh/aw9vliIh0aJ4M8CRgc4PtvLp9DT0EXGmMyQPmAb9u7kDGmCnGmExjTGZhYaE7apVWcrksd72znJDAAP5w4WAtByoi4ma+NohtIvCytTYZOBt4zRhzQI3W2hnW2gxrbUZ8fLzHi5QDzfx+E4s27OC+cwbRLUZTpIqIuJsnbyPLB1IabCfX7WvoemA8gLX2W2NMGBAHFHikQjki+bv28vi8NZzYtyuXH5dy+DeIiHQUNVWwaxPs3AA7NsDOXDjueujax+0f7ckAXwz0M8b0xgnuCcAVTdpsAk4HXjbGDALCAJ0j92HWWu6dswKXhScuHqJT5yLS8ezd1TigGz4vzQfr2t82KBz6nNaxAtxaW2OMuRn4BAgEXrTWrjLGPAJkWmvnAncCzxtjbscZ0DbZWms9VaO03pyl+Xy5rpAHz0vXVKki4p9cLti9pZmArtveu7Nx+4h46JwKPU+ALr2d5517O88jE8FDHRmPzsRmrZ2HMzit4b4HGjxfDZzoyZrkyBXsruCRD1Yzsldnrjkh1dvliEhHUVECRVlQtA4K1zrPS/PqXjRgApyQNAEH2TYteD0Aaved/t4ItZX7P98EQmxPJ5iPuWh/OHdOdR6hUZ79fRyEplKVI/bg+6vYW13Lk5cMISBAp85FpBWshd1b9wd00dq6wF4He7btbxcQDF37QmyKE7rW5bzXugDbZBvnp8t1kNdt4+cmEOIHQP/xjXvSMSkQ6Pvx6PsVik+at2IrH63cxm/HD6BvQqS3yxE5urlcULXH6bnue1SWNt6uKIGKXU22S50wC4+F8M4QFnuI552d7bBYpwfa0tPEtdXO6eiGAV20zgntqt3724VGQ1x/6Hs6xPWDuAFOuMb28osw9Qb9VqTVdpZV8cD7Kzk2KZopJ6V5uxyRjsdaKCtyThuX5DsDpUryoLy4mVCuC+uGA6maExwBYTH7H5HdnJAEJ9j37nI+a99zV/XBj2UC94d5w2Df99xVs/8U+I71zvY+0UlOQA+7wvkZP8AJbg9eO+4oFODSar//YDW7yqt59brjCQr0takERHyctc6gqNL8unBuGNJ126VbnOuzDQWGQKe4urCMgegekDCocSg3fIRG1z2PhbBoCGzFqoDWQlXZ/jDfu3P/84q67YbPy3c4Qb13p/MHhQmALmlOMA88ty6k+znbPnL9uCNQgEurzP+pgDnL8vn1z/qS3iP68G8Q8WXWQuVuJyybu6560GuoB3utwXZ50YHBvG+7urxxHSbQCeToJEgaCYPOh5hkZzsmCaKToVNXCPDQH8zGQGik84hJbt17911/1mlvt9NvWFpsd0U19767gn4Jkdz8s77eLkfk0KrKYPc2Z6DUoX42DdN2ZyCqmxPGienQ78y6UE7aH9KRCRAQ6OY6PCQgAN+b5LNjUoBLiz3+0U9sL61g+o1jCA3qIP/YiP+pqawL4H0hvLWZcN7mXBduKigcortDVHfoMdz5GZkIQWHN3HrUgtuQYP9rTduFd3GCOqp7605fi7SQAlxa5NucYmYu2sQvx/ZmeM/O3i5HOop9p7DLi53rqOXFzqnn8uL9j7IGz8uLDpxUA5zrw1HdnLBMGAR9frZ/u+HP0GgNlJIOQwEuh7W3qpa75yynV9dO3HnmAG+XI76uusIZMV2aXxfGO5wR1Q1DueGj6WCtfQKCneu+EXHQqQt0O9bZPiCYuzujnxXMcpRRgMth/eW/a9lYXM6bN4wmPESnzo9q1jqBXLLJCeldm52fJZvrHnlQdpDlC8I7OwHcqatzb2+P4fu3I+L2P9/3aM29xiJHIQW4HNKyTTt5ceEGrji+Jyf06ertcsTdaqqcOaHrgznvwLCu2dv4PUHhzixZMSnQbYjzMzbFGZwVEe+EcXhnjUoWaWf6f5QcVGVNLb99ezmJ0WHcc9ZAb5cjbVVT5QzyKt3ihHTpFijd6pzqLt3ihPPurTjrCDUQEe+EcsIg6P9zZ+R0TMr+n526qKcs4gUKcDmoZ/6XTVbBHl669jiiwjSK1qdV7q4L5C3NB/Turc2f2g7u5Nx/HNXdWQKxYTjH9nReCw73/PcRkcNSgEuzVm8p5bkvcrh4eBKnDUjwdjn+y1XrDNKqrYLamgbPq52frur9z1vapnJPXU86vy6gtzSeU3qf8C7Oaezo7pA0AqJ61E0W0n3/87AY9Z5F/JQCXA5QU+vit+/8SGynYH53brq3y/Ef1RWw9QfIW+w8Ni92esLtzQTWTQzSw5miss9pdb3ohgHdXT1nkQ5OAS4HmPH1elbmlzJ90gg6R4R4uxzfZK2zjnB9WH8P21bsXwAithf0GuPM/RwU4tynHBgCAUH7nwcG1z1CGvxsrk3TtqGem1JTRHyWAlwayS7Yw9OfZXHWsd04e3B3b5fjO6rKYMuy/T3rvMVQVuC8FtwJeoyAMTdD8nHOI1KXHUTEvRTgUq/WZbnrneWEBwfy8AXHeLsc77HWWVmpYe96+yqwtc7rXfo4M32l1IV1wjG6RUpEPE7/6ki9V7/NZcnGnfzl0qEkRIV5uxzPsNaZN7tgldPD3te73rvDeT0kyhkAdtIdTlgnZUCE7ocXEe9TgAvgTNjyx4/Xckr/eC4ekeTtctyjohQK1jhhvX31/ucN59aOGwADzt7fu44f2HFWiRKRDsWjAW6MGQ9MAwKBF6y1TzR5/SngtLrNTkCCtTbWkzUejb7fsINrX/qehOhQnrxkCMbfbyuqqYLirLqQrntsX+3MKLZPSJQzMcmg8yHxGEhId+baDtdCLSLiHzwW4MaYQOBZ4AwgD1hsjJlrrV29r4219vYG7X8NDPdUfUerb7KLuP6VTHrEhjHzhtEkRvvRqfN9I8EbhnTBaijK2j8aPCDIGQmechyMvGZ/WMf21P3PIuLXPNkDHwVkW2vXAxhjZgEXAKsP0n4i8KCHajsqfbG2gKmvLSG1awSv//J44qNCvV1S82prYNdGJ5iLs6BoHRT85JwCbziBSUyKE879f+4MLEtMh679nNu4REQ6GE8GeBKwucF2HnB8cw2NMb2A3sD/DvL6FGAKQM+ePdu3yqPEp6u3c9MbS+mXGMlr1x9PF1+437usuC6g9wV1tvNzx4b9PWpwFseIHwhDJzghnXAMJAx0ZhUTETlK+OogtgnA29buu2+nMWvtDGAGQEZGhm2ujRzcvBVbueXNZRyTFMOr144ippMH5zmvqYKdG5yQLloHxdn7A7vhYLLAEOiS5pz+HniO05OO6wdd+zqLZ4iIHOU8GeD5QEqD7eS6fc2ZANzk9oqOQu8ty+eOt35gRM/O7l+kxOWC1e9C/tL9Ib1z4/77qQEiE51wTr+gcUjH9tK91SIih+DJfyEXA/2MMb1xgnsCcEXTRsaYgUBn4FsP1nZUeGvxZu6as5zRvbvywjUZRIS68T9/wRr4z62weREEhTmTn3QbDMdeUhfUfZ2g1mlvEZEj4rEAt9bWGGNuBj7BuY3sRWvtKmPMI0CmtXZuXdMJwCxrrU6Nt6PXv9vI/e+t5KR+ccy4KoPwEDfd21xdAV//GRY8DaFRcOE/YMjlmrtbRKSdefQcpbV2HjCvyb4Hmmw/5Mmajgb/WrCB33+wmtMHJvDspBGEBbspvDd87fS6d+TAkAnw80chIs49nyUicpTTRcYO7rkvcnjy458Yf0w3/jZxOCFBbugJl++ATx+AZa9B51S46l1nrnAREXEbBXgHZa1l2udZPP1ZFucP7cFfLxtKUGA7h7e1sPId+PhuJ8RPvA1OuQtCOrXv54iIyAEU4B2QtZY/fbKW6V/k8IuRyTx5yRACA9p51rGdG+HDOyH7U2cpzavedQapiYiIRyjAOxhrLX/4cA3/WrCBiaN68uiFxxLQnuFdWwOL/gHzHwUMjH8SRt2gBT9ERDxMAd6BuFyWB+au5PXvNjF5TCoPnpfevguTbPkB/nMLbP0R+o+Hs/8MsSmHf5+IiLQ7BXgHUeuy3DtnBf/O3MzUk9O4+6yB7RfeVWUw/zH4bjpExMOlrzgTr2gxEBERr1GAdwA1tS5+8/Zy3l2Wzy0/68vtZ/Rvv/DO+hQ+uMNZinPktTDuIQjXCq8iIt6mAPdz1bUubpv1Ax+u2Mr/ndmfm3/Wr30OvKcAPr4HVr4NcQPg2o+h1wntc2wREWkzBbgfq6yp5eaZy/h09XbuO3sQN5yc1vaDWgvLXof/3g/V5XDqvTD2Ngjy0aVGRUSOUgpwP1VRXcuvXl/CF2sLeeSCY7j6hNS2H7QoGz64DXK/hp5j4LynIX5A248rIiLtTgHuhyqqa7n+lcV8k1PM4xcPZuKodlgTfe1HMHsyBIbCedNg+NWav1xExIcpwP3Q7MzNLMwu5o+/GMJlGe1wG9fy2fDuVOg+FCa+CVHd2n5MERFxKwW4n3G5LC99k8vQ5BguHZnc9gMu/pczo1rqWCe8Q6PafkwREXE7nSP1M19mFbK+sIxrT+zd9lvFFjwFH97hTMoyabbCW0TEj6gH7mdeWphLQlQoZw/ufuQHsRY+f9gJ8MGXwoXPQWBw+xUpIiJu1+IeuDFmsjHmsmb2X2aMubp9y5LmZBfs5qt1hVw1uteRLwvqcjm97gVPQcZ1cNEMhbeIiB9qTQrcBexoZn8RcHf7lCOH8tLCXEKCArji+CMcdV5bDe9OgcwXnaU/z/mrRpqLiPip1pxCTwWym9m/vu41caNd5VW8szSPC4f1oGvkEUyqUl3h3Ca27iM4/UE46Y52r1FERDynNQFeAvQGcpvs7wPsaa+CpHmzFm+motrFtSf2bv2bK3fDmxMhdwGc8xc47pftX6CIiHhUa86ffgT8yRhTP3rKGNMDeBKY196FyX41tS5e/SaXE9K6Mqh7dOveXL4DXjkfNn4DF89QeIuIdBCtCfDfAhFAjjEm0xiTiXNKPaLutcMyxow3xqw1xmQbY5q9bl43KG61MWaVMWZmK+rrsD5ZtZ0tJRVcN7aVve/d2+Dlc2D7KpjwBgw5YAyiiIj4qRafQrfWFhpjhgOTgBF1u6cDb1pr9x7u/caYQOBZ4AwgD1hsjJlrrV3doE0/4B7gRGvtTmNMQsu/Ssf10sIN9OzSiZ8NbMWvY2cuvHoBlBXBlW9D75PdVp+IiHheq+4Dt9ZWAP+qe7TWKCDbWrsewBgzC7gAWN2gzQ3As9banXWfV3AEn9OhLM/bRebGnfzu3HQCA1o4cUvBT/DahT4sXvQAACAASURBVFC9F65+H5Iz3FukiIh4XGvuA7/bGHN9M/uvN8a05BR6ErC5wXZe3b6G+gP9jTELjTHfGWPGH6SWKftO4xcWFrb0K/illxbmEhkaxGUZLZw2NX8pvHQWWBdc+5HCW0Skg2rNNfApwNpm9q8BprZPOQQB/YBTgYnA88aY2KaNrLUzrLUZ1tqM+Pj4dvpo31NQWsEHy7fwi5HJRIW1YLKV3AXOgLXQSLjuY0hMd3+RIiLiFa0J8B44veamtnBgT7o5+UDDpbOS6/Y1lAfMtdZWW2s3AOtwAv2o9Pp3G6lxWSaPST1843WfwOuXQHQPuO4T6JLm9vpERMR7WhPgBcDgZvYPAYpb8P7FQD9jTG9jTAgwAZjbpM17OL1vjDFxOKfU17eixg6jorqWNxZt4vSBCaTGRRy68cp3YNYVED/QOW0e3cMzRYqIiNe0JsDnAE/VjUQHwBgzAvgL8Pbh3mytrQFuBj7BOe3+lrV2lTHmEWPM+XXNPgGKjTGrgfnAb6y1LfnjoMOZ++MWisuquO5wE7cseRnevh6SR8E1cyGiq0fqExER7zLW2pY1NCYC+BA4mf1zoncBvgbOttaWuaXCw8jIyLCZmZne+Gi3sdZy1rSvsRY+vu2kgy8buvBv8OnvoO8ZcNmrENLJs4WKiIjbGWOWWGsPGJHcmvvAy4BTjTE/A0bW7V5irf1fO9Uodb5bv4Oftu3myUsGNx/e1sL//gBf/xmOuRgu+icEhXi+UBER8ZpW3QdujOkMJAKBQAgw1hgzFsBa+0j7l3d0enHhBjp3CuaCYc2MDbQWPrkPvnsWRlwD5z4FAYGeL1JERLyqxQFujDkO+BgwQDRQCCQA5cBWQAHeDjYVl/PZmu3cdGpfwoKbBLO18NFd8P0/4fhfwfgn4GCn10VEpENrzSC2PwHvAHHAXuBEoBewDGetcGkHL3+TS6AxXHVCr8YvuFzw4Z1OeJ9ws8JbROQo15oAHwY8Za11AS4gxFqbhxPej7mjuKPN7opq3srczDlDupMYHbb/BZcLPrgNMv8FJ94KZ/5B4S0icpRrTYDXAtV1zwvYPylLEU5PXNro7SV57Kmsabzmt6sW5v4alr4CJ90J4x5WeIuISKsGsS3H6YVnA98B9xpjAnAWIGluilVpBZfL8vI3uYzoGcuwlLrZY1218P5N8OObcMrdcOrdCm8REQFa1wN/FKipe/47nAFsHwEnAbe0c11Hnf/9VMDG4vL9ve/aGnh3qhPep90Hp92j8BYRkXqtuQ/8swbPc4FjjDFdgJ22pbPByEG99M0GuseEMf7YblBbDXNugFXvwukPOKfORUREGmhND/wA1todCu+2+2lbKQuzi7nqhF4EUwtvX+eE9xm/V3iLiEizWjWRi7jHywtzCQsOYOKIbjB7Mvz0Afz8cTjh/3m7NBER8VEKcC/bUVbFu8vyuXRYAp0/+CWs+wjO+hMcP8XbpYmIiA9TgHvZm99vgpoK7in9PWyaD+f8BY77pbfLEhERH6cA96LqWhf//mYts2P/TsSmpXDeNBg52dtliYiIH1CAe9Eny9bzWMWjDA5cDRc8A8Ov9HZJIiLiJxTg3lK5h16fXEt64Brshc9hhk30dkUiIuJH2nQbmRyhyt3sfvEi0qtWsHDwowQovEVEpJUU4J5WUQqvX0Kn7ZncZW5l5LkabS4iIq2nAPekvbvgtYuw+Uv4dfUtxGZcTkSormKIiEjrKT08Ze9OeO0i2LaSd/s+xscrkvlyTKq3qxIRET/l0R64MWa8MWatMSbbGHN3M69PNsYUGmN+qHt0jBuiy3fAK+fD9lVUXvIqj2T35oz0RFK6dPJ2ZSIi4qc81gM3xgQCzwJnAHnAYmPMXGvt6iZN/22tvdlTdbndvvAuWgcTZjJn5wB2la/guoZrfouIiLSSJ3vgo4Bsa+16a20VMAu4wIOf73nVe2Hm5U54T3wT23ccLy3cQHr3aEb17uLt6kRExI95MsCTgM0NtvPq9jV1iTFmuTHmbWNMSnMHMsZMMcZkGmMyCwsL3VFr27lq4Z1fQt5iuOR56Hs6C7OLWbd9D9eN7Y3R2t4iItIGvjYK/T9AqrV2CPAp8Epzjay1M6y1GdbajPj4eI8W2CLWwsf3OKuKjX8c0p0TDS8u3EBcZAjnDe3u5QJFRMTfeTLA84GGPerkun31rLXF1trKus0XgJEeqq19ffsMfP9PGH0TjL4RgA1FZfzvpwImHd+L0KBALxcoIiL+zpMBvhjoZ4zpbYwJASYAcxs2MMY07JqeD6zxYH3tY+Uc+O/9kH4hnPmH+t0vL9xAcKBh0uieXixOREQ6Co+NQrfW1hhjbgY+AQKBF621q4wxjwCZ1tq5wC3GmPOBGmAHMNlT9bWL3IXw7lToeQJc9E8IcP4+KtlbzewleZw3tAcJUWFeLlJERDoCj07kYq2dB8xrsu+BBs/vAe7xZE3tpnAtzJoIsb1gwkwI3h/UszM3U15Vq1vHRESk3fjaIDb/tHsbvP4LCAyFK9+GTvtvEat1WV7+JpdRqV04NinGi0WKiEhHogBvq8rdMPMyKC+GSW9B59RGL3+xtoC8nXu59sTUZt8uIiJyJDQXelvUVsPsybBtJUycBT2GH9Bk/toCIkICGZee6Pn6RESkw1KAHylr4YPbIfszOO9v0P/MZpstyCpidFpXggN1skNERNqPUuVIffUnWPYanPwbGHlNs0027ygnt7icsf3iPFyciIh0dArwI7HsDZj/KAydCKfdd9BmC7KLABjbVwEuIiLtSwHeWtmfw39ugbRTnVPnh5jTfEF2EYnRofRNiPRYeSIicnRQgLfG1uXw1jUQPxAuew2CQg7a1OWyfJNdxNi+8Vq4RERE2p0CvKV2bYY3LoWwaJg02/l5CKu2lLKzvJqTdP1bRETcQKPQW2LvTnjjF8763td9DNE9DvuWfde/x/Tt6u7qRETkKKQAP5yaSph1JRTnwFVzIDG9RW9bkF3IwG5RmvtcRETcQqfQD8Xlgvf+H2xcABc+B71PbtHbKqprWZy7U6PPRUTEbRTgh/L5w7DybTj9QRhyaYvf9v2GHVTVuDhR179FRMRNFOAH8/3zsPBpyLgext7eqrcuzC4iJDCA43t3OXxjERGRI6AAb85P8+Cj30L/s+CsPx7yXu/mfJ1VxIhesXQK0RADERFxDwV4U3mZ8PZ10H0Y/OJfENi6EC7aU8nqraWc1C/eTQWKiIgowBsrznGWBo1KhCvegpCIVh/im5xiAE7UADYREXEjBXhDy99yVhmb9A5EHlkPekFWITHhwQxOimnn4kRERPbTRdqGTr0bRlwFMclH9HZrLQuyihjTpyuBAZo+VURE3Ec98IaMOeLwBlhfVMaWkgqdPhcREbfzaIAbY8YbY9YaY7KNMXcfot0lxhhrjMnwZH1ttbBu+lTNfy4iIu7msQA3xgQCzwJnAenARGPMAfOSGmOigFuBRZ6qrb18nVVESpdwenVt/eA3ERGR1vBkD3wUkG2tXW+trQJmARc00+73wJNAhQdra7OaWhff5RQztq9uHxMREffzZIAnAZsbbOfV7atnjBkBpFhrPzzUgYwxU4wxmcaYzMLCwvav9Aj8mLeL3ZU1mv9cREQ8wmcGsRljAoC/Ancerq21doa1NsNamxEf7xs93gVZxRgDY/po+VAREXE/TwZ4PpDSYDu5bt8+UcCxwBfGmFxgNDDXXwayLcguZHBSDJ0jQrxdioiIHAU8GeCLgX7GmN7GmBBgAjB334vW2hJrbZy1NtVamwp8B5xvrc30YI1HZE9lDcs27dLtYyIi4jEeC3BrbQ1wM/AJsAZ4y1q7yhjziDHmfE/V4Q6L1hdT47KcpAAXEREP8ehMbNbaecC8JvseOEjbUz1RU3v4OquIsOAARqZ29nYpIiJylPCZQWz+bEF2EaN6dyU0KNDbpYiIyFFCAd5GW0v2kl2wh7F9NfpcREQ8RwHeRguzneVDNYGLiIh4kgK8jRZkFRIXGcLAblHeLkVERI4iCvA2sNayILuYMX3iCNDyoSIi4kEK8DZYu303RXsqGavVx0RExMMU4G2wIEvLh4qIiHcowNvg66wi+sRH0D0m3NuliIjIUUYBfoQqa2pZtKFYq4+JiIhXeHQmto5k6cZdVFS7GNtPt4+JiHu4XC7y8vIoKyvzdiniJhERESQnJxMQ0Pr+tAL8CC3ILiQwwDA6rYu3SxGRDqqoqAhjDAMGDDiif+DFt7lcLvLz8ykqKiIhIaHV79f/Io7QgqwihqXEEhUW7O1SRKSD2rVrF4mJiQrvDiogIIDExERKSkqO7P3tXM9RoaS8muX5Jbr+LSJuVVtbS3CwOgkdWXBwMDU1NUf0XgX4EfgmpwhrdfuYiLifMZokqiNry39fBfgR+Dq7iMjQIIamxHq7FBEROUopwI/AgqwiRqd1IThQvz4REfEOJVArbSouZ9OOcl3/FhHxkJdffpmgIN001ZQCvJUWZDvTp+r+bxGRgxs3bhyTJ09ul2Ndfvnl5Ofnt8uxOhL9SdNKC7IL6RYdRp/4CG+XIiLi16qqqggJCTlsu/DwcMLDNWV1U+qBt0Kty/JNTjFj+8VpZKiIyEFMnjyZzz//nFdeeQVjDMYYXn75ZYwxvPHGG5x99tlERETwu9/9DmstN9xwA3369CE8PJy0tDTuvfdeKisr64/X9BT6vu2FCxcyYsQIOnXqxMiRI1m8eLE3vq7XeLQHbowZD0wDAoEXrLVPNHn9V8BNQC2wB5hirV3tyRoPZdWWEnaVV+v2MRHxiof/s4rVW0o9/rnpPaJ58LxjWtx+2rRprF+/nu7duzNt2jQASkuduu+66y6efPJJnn32WQCstSQkJDBz5kwSExNZvnw5U6dOJTg4mIcffvign+FyubjnnnuYNm0a8fHx3H777Vx22WVkZWUdNdfLPfYtjTGBwLPAGUAesNgYM7dJQM+01v6jrv35wF+B8Z6q8XC+rls+dEwfBbiIyMHExMQQEhJCeHg43bp1A6CiogKAqVOnMmnSpEbtH3300frnqamp5OTkMH369EMGuLWWp59+mhEjRgDw0EMPMXr0aHJychgwYEB7fyWf5Mk/U0YB2dba9QDGmFnABUB9gFtrG/5pGQFYD9Z3WAuyihjYLYr4qFBvlyIiR6HW9IJ91ahRow7Y9/zzz/PCCy+Qm5tLWVkZNTU1uFyuQx7HGMPQoUPrt3v06AHA9u3bj5oA9+Q18CRgc4PtvLp9jRhjbjLG5AB/BG5p7kDGmCnGmExjTGZhYaFbim1qb1UtSzbu1OlzEZE2iIhoPAB49uzZ3HTTTVx++eXMmzePZcuW8cADD1BdXX3I4wQEBBAYGFi/vW9c0uGCvyPxuQsF1tpngWeNMVcA9wPXNNNmBjADICMjwyO99O9zd1BVq+VDRURaIiQkhNra2sO2++qrrxg+fDh33HFH/b7c3Fw3VtZxeLIHng+kNNhOrtt3MLOAC91aUSssyCokJDCAUalaPlRE5HB69+7NkiVLyMnJoaio6KA96gEDBrBixQref/99cnJymDZtGnPmzPFwtf7JkwG+GOhnjOltjAkBJgBzGzYwxvRrsHkOkOXB+g7p66wiRvbqTHhI4OEbi4gc5e68807i4uIYOnQo8fHxLFy4sNl2U6dO5aqrruLaa69l+PDhLFq0iIceesizxfopY63nxokZY84Gnsa5jexFa+2jxphHgExr7VxjzDRgHFAN7ARuttauOtQxMzIybGZmplvrLtxdyXGPfsZvfj6Am07r69bPEhHZZ82aNQwaNMjbZYibHe6/szFmibU2o+l+j14Dt9bOA+Y12fdAg+e3erKelvomx7l9TAPYRETEV2gmthb4OquImPBgjukR4+1SREREAAX4YVlrWZhdxIl9uxIYoOlTRUTENyjADyOnsIytJRWM7avbx0RExHcowA9jQZYzUYzW/xYREV+iAD+MBdlF9OzSiZ5dO3m7FBERkXoK8EOornXx3fodjNXocxER8TEK8EP4cfMu9lTWcJJOn4uIiI9RgB/C11lFGAMn9Onq7VJEREQaUYAfwsLsIoYkxRDbKcTbpYiIHFVefvllgoL2zzX2xRdfYIwhLy/vkO8zxvD666+3+fMnT57MuHHj2nwcd1KAH8TuimqWbd6l698iIj5gzJgxbN26tX7d7/by+uuv1y9F2tC0adOYPXt2u35We/O55UR9xXfrd1Drspyo698iIl4XEhJCt27dPPZ5MTG+P/OmeuAHsSCrkPDgQEb26uztUkRE/Mrzzz9PTEwMFRUVjfY/+eST9OzZk9raWm644Qb69OlDeHg4aWlp3HvvvVRWVh70mM2dQp8/fz5DhgwhLCyMIUOGMH/+/APed9999zFo0CA6depESkoKv/rVrygpKak/5lVXXQU4p96NMUyePBk48BS6tZY///nPpKWlERISQp8+fXj66acbfVZqaioPPPAAt956K126dCExMZHbb7+dmpqa1v0CW0g98INYkF3EqN5dCA3S8qEi4iM+uhu2rfD853YbDGc90eLml112Gbfccgvvv/8+l19+ef3+V199lSuvvBJjDAkJCcycOZPExESWL1/O1KlTCQ4O5uGHH27RZ2zZsoVzzz2Xyy67jFmzZpGfn8+ttx64HlZ4eDgzZswgJSWFnJwcbrrpJm655RZeeeUVxowZwzPPPMPNN9/M1q1b69s3Z/r06fzud79j2rRpnHbaaXz++efcdtttREVFcf3119e3+/vf/85dd93FokWLWLZsGZMmTeLYY49t1Ka9KMCbsbVkLzmFZUwc1dPbpYiI+J2YmBguuOACXn311foAz8zMZPXq1cyZM4eAgAAeffTR+vapqank5OQwffr0Fgf49OnTiYuL4/nnnycoKIj09HQee+wxzjvvvEbt7r///kaf8/jjjzNhwgReeuklQkJC6k+VH+70/BNPPMGvf/1rpkyZAkC/fv1Yu3Ytjz76aKNwPumkk7j77rvr27z00kt89tlnCnBP+TrLWT5U179FxKe0ohfsbddccw3nn38+BQUFJCQk8OqrrzJq1CgGDBgAOKfZX3jhBXJzcykrK6OmpgaXy9Xi469evZpRo0Y1Gqk+duzYA9rNmTOHp59+muzsbEpLS3G5XFRVVbFt27YWD4grLS0lLy+Pk08+udH+U045hWnTplFeXk6nTs5sncOGDWvUpkePHmzYsKHF36s1dA28GQuzi4iLDGVgtyhvlyIi4pfOPPNM4uLimDlzJtXV1cyaNYtrrrkGgNmzZ3PTTTdx+eWXM2/ePJYtW8YDDzxAdXV1u9awaNEiLr30Uk4++WTeffddli5dyj/+8Q8Aqqqq2vWz9gkJaXzbsTGmVX+YtIZ64E24XM7yoWP7xjV7a4GIiBxeYGAgkyZN4rXXXiMtLY2SkhImTJgAwFdffcXw4cO544476tvn5ua26vjp6em89tpr1NbWEhjojFVauHBhozYLFiwgLi6OP/zhD/X73n777UZt9gVuw+M0FR0dTXJyMl999RXnnntu/f4vv/yS3r171/e+PU098CZ+2raboj1VOn0uItJGV199NUuXLuXBBx/k3HPPpUuXLgAMGDCAFStW8P7775OTk8O0adOYM2dOq4594403UlhYyJQpU1izZg2ff/459913X6M2AwYMoLCwkH/961+sX7+eV199lenTpzdq07t3bwDmzp1LYWEhe/bsafbz7rnnHv7+97/z/PPPk5WVxT//+U+ee+457r333lbV3Z4U4E0syHaWDz2pn9b/FhFpiyFDhjBs2DB++OEHrr766vr9U6dO5aqrruLaa69l+PDhLFq0iIceeqhVx05KSuI///kP33//PcOGDePWW2/lr3/9a6M25557Lvfddx/33nsvgwcPZtasWfzpT39q1Oa4447j1ltvZerUqSQkJHDzzTc3+3k33ngjjzzyCI899hjp6ek8+eSTPPHEE24ZnNZSxlrrtQ9vDxkZGTYzM7Pdjnf1i9+zZddePrvjlHY7pojIkVizZg2DBg3ydhniZof772yMWWKtzWi636M9cGPMeGPMWmNMtjHm7mZev8MYs9oYs9wY87kxppcn66uoruX7DcWM1elzERHxcR4LcGNMIPAscBaQDkw0xqQ3abYMyLDWDgHeBv7oqfoAlm7cSUW1SwEuIiI+z5M98FFAtrV2vbW2CpgFXNCwgbV2vrW2vG7zOyDZg/WxILuIoADDaC0fKiIiPs6Tt5ElAZsbbOcBxx+i/fXAR26tqIlfndqHk/vHExmqu+tERMS3+WRSGWOuBDKAZkeSGWOmAFMAevZsv+lOo8OCGZ2m3reI+A5rreak6MDaMpDck6fQ84GUBtvJdfsaMcaMA+4DzrfWNrs0jbV2hrU2w1qbER+v271EpGMKDAxs99nJxLdUV1c3mg62NTwZ4IuBfsaY3saYEGACMLdhA2PMcOCfOOFd4MHaRER8TmxsLNu3b3fbVJziXS6Xi+3btx/x2uMeO4Vura0xxtwMfAIEAi9aa1cZYx4BMq21c4E/AZHA7LpTRpusted7qkYREV8SFxdHXl4ea9eu9XYp4iYRERHExR3ZnU+ayEVERMSH+cRELiIiItI+FOAiIiJ+SAEuIiLihxTgIiIifkgBLiIi4of8fhS6MaYQ2NiOh4wDitrxeL5A38k/dLTv1NG+D+g7+YuO9p16WWsPmLXM7wO8vRljMpsbru/P9J38Q0f7Th3t+4C+k7/oiN+pOTqFLiIi4ocU4CIiIn5IAX6gGd4uwA30nfxDR/tOHe37gL6Tv+iI3+kAugYuIiLih9QDFxER8UMKcBERET+kABcREfFDCnARERE/pAAXERHxQwpwERERP6QAFxER8UMKcBERET8U5O0C2iouLs6mpqZ6uwwRERG3WLJkSVFzq5H5fYCnpqaSmZnp7TJERETcwhjT7JLZOoUuIiLihxTgIiIifkgBLiIi4ocU4CIiIn5IAS4iIuKHFOAiIiJ+SAEuIiLihxTgIiIifkgB3sT20gpvlyAiInJYCvAGnvp0HWc+9RUl5dXeLkVEROSQFOAN/PyYbpRWVPPM/CxvlyIiInJICvAG0ntE84sRybzyzUY2FZd7uxwREZGDUoA3ceeZAwgMMPzxk5+8XYqIiMhBKcCb6BYTxg0np/HB8q0s3bTT2+WIiIg0SwHejKknpxEXGcpjH67BWuvtckRERA6gAG9GRGgQd57Zn8yNO/lk1TZvlyMiInIABfhBXDoymf6JkTzx0U9U1bi8XY6IiEgjCvCDCAoM4J6zB5FbXM4bizZ6uxwREZFGFOCHcGr/eMb2jWPa51mU7NXkLiIi4jsU4IdgjOGeswdSsrea6fOzvV2OiIhIPQX4YRzTI4ZLRiTz0sJcNu/Q5C4iIuIbFOAtcOeZ/QkIgD9+stbbpYiIiAAK8BbpHhPODSel8Z8ft7BMk7uIiIgPUIC30NRT+hAXGcJj8zS5i4iIeJ8CvIUiQ4O4/Yz+LM7dySertnu7HBEROcp5LMCNMS8aYwqMMSsP8nqMMeY/xpgfjTGrjDHXeqq2lro8I4W+CZE88dEaTe4iIiJe5cke+MvA+EO8fhOw2lo7FDgV+IsxJsQDdbVYUGAA9549kNzicmZqchcREfEijwW4tfYrYMehmgBRxhgDRNa1rfFEba1x2oAETuzbVZO7iIiIV/nSNfBngEHAFmAFcKu1ttnz1MaYKcaYTGNMZmFhoSdrxBjDvWcPYtfeaqZ/ocldRETEO3wpwH8O/AD0AIYBzxhjoptraK2dYa3NsNZmxMfHe7JGwJnc5eLhmtxFRES8x5cC/FpgjnVkAxuAgV6u6aD+7+f9McCf/6vJXURExPN8KcA3AacDGGMSgQHAeq9WdAj7Jnd5/4ct/Lh5l7fLERGRo4wnbyN7E/gWGGCMyTPGXG+M+ZUx5ld1TX4PjDHGrAA+B+6y1hZ5qr4j8atTncldHtXkLiIi4mFBnvoga+3Ew7y+BTjTQ+W0i8jQIG4b15/731vJp6u3c+Yx3bxdkoiIHCV86RS6X5pwXAp94iN44qOfqK7V5C4iIuIZCvCGynfAT/Na9RZncpdBrC8qY+aiTW4qTEREpDEFeENf/wVmTYQvnoBWXNP+2cAETkjrytOfraO0QpO7iIiI+ynAGzr9ARg6Eb54HN6+Fqpado+3MYb7zhnEzvJqps/PcXORIiIiCvDGgkLhwudg3MOw6j14+Wwo3dKitx6bFMPFw5N4ceEG8nZqchcREXEvBXhTxsDY22DCTCjKgud/BvlLW/TWO38+wJnc5RNN7iIiIu6lAD+YgWfDdZ9AQDC8dBasnHPYtyTFhnP92N6898MWludpchcREXEfBfihdDsWbvgfdB/mXBOf/zi4Dn2r2I2n9qFrRAiPfqjJXURExH0U4IcTGQ/XzIWhV8CXTxx2cFtUWDC3jevHog07+GxNgQcLFRGRo4kCvCWCQuHC6XDG72H1+84p9UMMbpswqid94iN4/KM1mtxFRETcQgHeUsbAibfAxFlQnA0zToP8Jc02DQ4M4J6zBrG+sIxZ32tyFxERaX8K8NYaMB6u/y8EhcBLZ8PKd5ptdvqgBEandeGpz7I0uYuIiLQ7BfiRSDwGbpgPPYbD29fB/McOGNxmjOG+s9PZUVbFP77Q5C4iItK+FOBHKiIOrn4fhl0JXz4Jb08+YHDb4OQYLhqexAsLNrBk407v1CkiIh2SArwtgkLhgmfgzD/A6rnw0ngoyW/U5J6zBtI9JoxrXvyezNwdXipUREQ6GgV4WxkDY34NV/wbitfD86dB3v7BbQnRYfx7ygkkRIVy9f9v787joyrvPY5/fkkIBBKykIQEQgiQgGyyyCabIMpmBa9at9raaqXWrd6u3nrb2s3udV/qdalbwSpVUVFRRAEV2WQXCHvCEhLClJn08AAAIABJREFUEgIhJHnuH2eQGBMIMJkl+b5fr3nNzDnPzPwOk+TLOec5z/PUQj7dtCeIxYqISGOhAPeXruPgu+9CVAtvDPWVL3+xKi2+BdOmDCE9vgXffnoRH28sCmKhIiLSGCjA/Sm1u69zW3+YfgO8/7svOreltm7BtCnnkpEYw/X/XMT8XIW4iIicPgW4v7Vq43Vu6/dNmPsXeOk6KC8FICWuOdOmDCGrTSuuf2YRH6zTSG0iInJ6FOANISoaJj0I4+6BtW/AU+OgeBMAbWKb868bh5CdEsuUZ5cwZ61CXERETp0CvKGYwbm3wNUvwr5t8NgIWD4NgKRW0fzrxsF0TYtlynOLeXdNQZCLFRGRcBOwADezp8xst5mtOkGbUWa2zMxWm9mHgaqtQXUdC9//GNL7wCvfg+k3QtkBElpG88INQ+iR3prvP7+Et1ftCnalIiISRgK5B/5PYHxdK80sAXgEmOSc6wl8PUB1Nbz4DLjudRh9lzf06mPDIW8R8S2b8dx3B9M7I55b/rWUN1fsDHalIiISJgIW4M65ucCJRjK5BviPc26br33jOjkcEQnn/RS+8xY4550Xn/tXWkdH8Oz1g+jXIYHbp33GjOV1z3ImIiJyTCidA+8KJJrZB2a2xMy+FeyCGkTmYLhpHvSYDO//Fp6dTFx5Ic9cP4hzOiZyx7TPeOWz/GBXKSIiIS6UAjwKOAe4CBgH/MLMutbW0MymmNliM1tcWFgYyBr9IyYBLn8KJj8M25fCo0Nptfkd/vmdgQzu1IYf/ns5Ly9RiIuISN1CKcDzgXecc6XOuSJgLtCntobOucedcwOccwNSUlICWqTfmEG/a+F7cyEhE6ZdQ8tZP+Wpb/RiWJdkfvLycl5cpLnERUSkdqEU4K8Bw80sysxaAoOBz4NcU8NLzoYb3oNzb4XFTxLzzwt4ckJLRuak8LPpK3nh063BrlBEREJQIC8jmwp8AnQzs3wzu8HMbjKzmwCcc58DbwMrgIXAE865Oi85a1SiomHc7+Ha/8ChPTR/agxPdP+M87ulcNcrq3j2ky3BrlBEREKMOeeCXcMZGTBggFu8eHGwy/Cfg4Xw2s2QO4vKnPH86MgUXl1fxi+/1oPrh3cKdnUiIhJgZrbEOTeg5vKoYBQjJxCbAtf8Gz59jMh3f8m9Mcvo2OnH/OYNqKxy3Diyc7ArFBGREKAAD0VmMOT70HEYNv0G7tj5E3qmX8XNMyuoqHJ8f1SXYFcoIiJBFkqd2KSm9LNhygdY/28xdu9UZifcw9R3PuSh93ODXZmIiASZAjzURbeCSQ/A158hk53Mivk5G997kvveWx/sykREJIgU4OGi5yXYTR/RPKMv90Y/SscP7+B30xdQUVkV7MpERCQIFODhJKED9u03qRr1cyZHfsJ1y6/lD48/w4Gyo8GuTEREAkwBHm4iIokY9TMirn+bxFbR/HzXf/PavbeSV3Qg2JWJiEgAKcDDVeZgYn+wgD2dJ/PNI9MofmgMK1ctD3ZVIiISIArwcNaiNanXPcOuCx+mM/l0emkcS19/1JuuVEREGjUFeCOQNuxaKqfMZ1t0F/ovuZN1D1+BO7w32GWJiEgDUoA3EgntutDlJ3N4M+W7dCl8j71/G8yRjfOCXZaIiDQQBXgj0jw6mok3/5VX+j/NgXKIem4Sh96+GyrVS11EpLFRgDcyZsbXJ1/C2ktm8krVSFouuJeyf4yBPRuDXZqIiPiRAryRGt8/m+wbn+FnET/mSMEGKh8dDkufUwc3EZFGQgHeiPXtkMDtt/+YW+IfYmF5Fsy4FV66Dg4VB7s0ERE5QwrwRq59QgyP3TKJJzrdyx+OXk3lmjdwjw6DzXODXZqIiJwBBXgTENs8isevG0z5kNuYfOTXFByOwD0zCd79FVSUB7s8ERE5DQrwJiIywvjVxT25cvIkLjj0W2ZGj4OP7oMnL4AiTU8qIhJuFOBNzDeHdOThb4/gziPX8+PIn1JRvA0eGwGLn1YHNxGRMKIAb4LO65rC9JuHsiD6XEaX/p7CpH7wxh0w7RtQsivY5YmISD0owJuorm3jePWWYaS0y2LQtpuZ3+WHuA3vwgP9YM49cORgsEsUEZETUIA3YcmxzfnXjUO4uE8G164ewB86PUNlzlj48E9ekC96Eiorgl2miIjUImABbmZPmdluM1t1knYDzazCzC4PVG1NWYtmkdx/VV/uuCCHx1c5vrHvJg5e+w606QJv/hAeGQJr39T5cRGREBPIPfB/AuNP1MDMIoE/AbMCUZB4zIw7LujKfVf2ZcnWvUx+rYy8ydPhqn95DaZdA09PhPzFwS1URES+ELAAd87NBU42BNhtwHRgd8NXJDVd0q89z90wmMKSI/zXox+zrNUwuHkBXPR32JMLT4yBl74NxZuCXaqISJMXMufAzaw98F/Ao/VoO8XMFpvZ4sLCwoYvrgkZ0rkN/7l5KC2aRXLV458wa20RDLwBbv8MzvsZrH8HHhoEb90JpXuCXa6ISJMVMgEO3Af8zDlXdbKGzrnHnXMDnHMDUlJSAlBa05KdGscrNw+jW1prvvf8Ep7+aDM0j4PRP/eCvO81sPAfXke3+ffC0cPBLllEpMkJpQAfAEwzsy3A5cAjZnZJcEtqulLimjPtxiFc2L0tv359Db9+fTWVVQ7i0mDSA/D9T6DjufDe3fDgAFg2FapO+n8vERHxk5AJcOdcJ+dclnMuC3gZuNk592qQy2rSYqIjefTac7h+WCee/mgL339+CYfLK72VqWfBNS/CdW9Aq2R49SZ4fCRsfD+4RYuINBGBvIxsKvAJ0M3M8s3sBjO7ycxuClQNcuoiI4xfXtyDX13cg3c/L+Cqxz+hsOTI8QadRsCNc+CyJ6FsPzz3X/DcpbDrhFcLiojIGTIX5tf3DhgwwC1erMubAmHW6l3cPu0zkmOb88/vDCQ7Ne7LDSqOwML/g7l/8cK87zUw+i6Ibx+cgkVEGgEzW+KcG1BzecgcQpfQN7ZnGi9OOZeyo1Vc+sjHfLKxRi/0qOYw9Fb4wTLvfuVL8GB/mPW/cFBXC4iI+JMCXE5Jnw4JvHLzUFJbt+BbT33KK5/lf7VRTCKM/R3cuhh6XAKfPAz3nw2zfqEgFxHxEwW4nLIOSS2ZftNQzumYyH+/uJwHZudS66mYxI5w6T/gloXQ/WL45CEFuYiInyjA5bTEt2zGs9cP5tJ+7fn7u+v56csrOFpZx2VkyTlw6eMKchERP1InNjkjzjnuey+X+2fnMjw7mUeu7U/rFs1O/KKiXK+j28qXIKoFDPwuDL0dYjUoj4hITXV1YlOAi1+8vCSfO6evoHNKK57+ziDaJ8Sc/EUKchGRk1IvdGlQl5+TwTPXD2Ln/jIuefgjVm3ff/IX6dC6iMhpU4CL3wzLTmb694cSHRnBFf/4hNmfF9TvhQpyEZFTpgAXv+raNo5Xbh5K55RW3PjsYp77ZEv9X6wgFxGpN50DlwZReqSC26d+xuy1u7lmcCa/uKgHMdGRp/Ymheu9c+SrXtY5chFpstSJTQKussrx57fX8o+5m+iS0or7r+pHr/bxp/5GCnIRacIU4BI083OL+NFLyyguLecn47rx3eGdiYiwU3+j6kEe2RzOuQ6G3gbxGf4vWkQkRCjAJaj2lpZz539W8M7qAoZlt+FvX+9LWnyL03uzolyYfx+smAYY9LkShv03JGf7tWYRkVCgAJegc87x4qI8fv36Gpo3i+CPl57N+F5pp/+G+/Lg4wdg6bNQWe6Nuz7ih5DW239Fi4gEmQJcQsbGwoPcMW0ZK7fv5+pBHfjF13rQMjrq9N/w4G5Y8AgsfALKSyBnHIz8MXQY5L+iRUSCRAEuIaW8oop731vPYx9upFMbr4Nb74zT6OBW3eG9XogveAQOF0PWCG+PvPNosNM45y4iEgIU4BKSPt5YxA9fXE7RwSP8aGw3pozsTOTpdHCrrrwUljzjHV4v2Qnt+sOIH0G3iRChoQ9EJLwowCVk7TtUzs9fWcnMlbsY0jmJe6/sS3p8PcZSP5mKI7B8Ksy/F/ZugZSzYPgPoddlEHkGh+xFRAJIAS4hzTnHS0vyuXvGappFRvCHS3szsXe6f968sgLWvArz/ga710BCRxh+B/S5BpqdZk94EZEAaZAAN7NY4DxgvXMu9wzqO20K8MZlS1EpP5j2Gcvz9/P1czK4e1JPWjX3095yVRWsfxvm/RW2L4HYNO868nO+Dc1j/fMZIiJ+5pcAN7N/AQuccw+YWTNgGdAdqAAudc694a+C60sB3vgcrazi/vdyefiDDXRMasl9V/Wjb4cE/32Ac7D5Q2+PfPNciEmEwd+HQTdCyyT/fY6IiB/4azrRUcBHvscXA3FAOnA38IuTFPCUme02s1V1rP+Gma0ws5Vm9rGZ9TnF2qSRaBYZwY/HdWPajUM4Wum4/NGPeXjOBiqr/HS6xww6j4LrXocb3oPMc+GDe+DvPWDGbbBzhX8+R0SkAZ3qHngZkO2cyzezR4Ejzrk7zCwLWOGca32C144EDgLPOud61bJ+KPC5c26vmU0A7nbODT5ZTdoDb9z2Hz7KXa+s5I0VOxnUyevg1j7BDx3caipYAwv/ASv+DUcPQYch3h5590kQFe3/zxMRqSd/7YEXAp18jy8E5vgetwSqTvRC59xcoPgE6z92zu31PV0AaIBrIT6mGQ9e3Y+/X9GHNTsOMP6+uby+fIf/P6htD7j4fvjhGhh3DxwsgOk3wH29YM49cGCn/z9TROQMnGqAvwS8YGbvAa2Bd33L+wL+7MR2A/BWXSvNbIqZLTazxYWFmie6sTMzLu2fwczbR5CdGsttUz/j5heWkFd8yP8fFpMI594Cty2Fb0yH9L7w4Z+9IH/p27DlI+8cuohIkJ3qIfQo4HYgE3jaObfct/zHwAHn3OMneX0W8EZth9CrtRkNPAIMd87tOVlNOoTetFRUVvHYhxt5eM5GKqsc1w/vxC2juxDXolnDfWjxZlj8JCx9Dsr2QWpP7/D62VdAdKuG+1wREULkOvCTBbiZnQ28Akxwzq2vz3sqwJumXfvL+Ms765i+NJ82raL54diuXDmgA1GRDTjSWvkhbyrThY/DrpXQPB76fcObn7xNl4b7XBFp0vx1GVkfoMI5t9r3fCLwHWA18DvnXMVJXp9FHQFuZpnA+8C3nHMf17cmBXjTtjJ/P799Yw0LtxTTtW0s/3tRD0Z2TWnYD3UO8hZ6Qb7mVaiqgC5jYNAUyLkQIiIb9vNFpEnxV4AvAO5zzk0zswxgPfAhcDbwnHPuzhO8direZWjJQAHwK6AZgHPuMTN7ArgM2Op7SUVtBdekABfnHO+s3sU9M9eyrfgQo7qlcNfE7uS0jWv4Dy8pgKXPwOKnvHHXEzK9PfJ+39Q15SLiF/4K8L3AEOfcOjO7HbjMOXeemY0BnnDOdTrJW/idAlyOOVJRybMfb+WB93M5VF7JNYMy+e8Lu5LUKgCXgVUehbVveLOhbZ0PUS2g1+XeKG/tz9EkKiJy2vwV4KVAD+fcVjP7D7DQOfdHM+uAN5xqA1yge2IKcKlpz8Ej3D87lxc+3UbL6EhuOz+b64Zm0TwqQIe2C1bDwv+DFS9615THtoWu473Z0DqfB80C/msiImHMXwG+FHgB73Ky1cBo59xiMxsEvOqca+evgutLAS51yS0o4Z6ZnzNnXSEd27TkfyacxbieaVig5gYv2w/r34F1MyH3PSgvgagY6HI+dBvvhXpsamBqEZGw5a8Anwz8G4gCZjnnJviW/y9wrnPuIj/VW28KcDmZuesL+d2ba1hfcJBBnZL4xUU96J0RH9giKsq9Q+vr3vJu+/MAg4wB0G2Ct3eecpY3zKuISDV+u4zMzNrijX++wjlX5Vt2LrDfObfGH8WeCgW41EdFZRUvLs7j77PWs6e0nEv7t+en484iLT4I04k6BwWrfGE+E3Z85i1PzPKCvNsEb3z2yAa8tl1EwobfrwM3sxYAzrmyM6ztjCjA5VSUlB3l4TkbeWr+ZiIjjCkjO/O98zrTMtpPU5aejgM7vGlO170Fmz6EyiPQIh5yxnphnn2B91xEmiR/7oF/B7gLyPIt2gz83jn3zzOs8bQowOV05BUf4o9vr+XNFTtp27o5Pxl3Fpf2a09ERJAPYR85CJvmeGG+/m04tAcioiBruLd33nU8JHYMbo0iElD+Ogf+A+CPwKN413+Dd23394CfOecePPNST40CXM7E4i3F/PaNNSzP38/ArEQeuLof6fEh0ku8qhLyF3mH2de9BUW+wQlTe3oDxuSMhQ6DdKhdpJHzV4BvAP5cc8xzM/se8BPnXPYZV3qKFOBypqqqHNOX5nP3jNVER0Vw75V9GdUtBHuHF22A9W/Burchb4E3Alzz1tBltBfm2RdAXFqwqxQRP/NXgB/Buw58Y43l2cBq51zzM670FCnAxV82Fh7klheWsnZXCbeM7sJ/X9C1YcdWPxNlB2DTB5A7C3LfhYO7vOVpZ3thnjPW6+GuYV1Fwp6/AnwjcI9z7skay78L/I9zLuAzOijAxZ/KjlZy94zVTFuUx6BOSTx4dT/atg5CT/VTcaxX+7Ewz1sIrhJaJED2GC/Mu4yB2AYeI15EGoS/AvzHwK+BB4B5vsUjgduAXzrn/uaHWk+JAlwawiuf5fPz/6yiZXQk913VlxE5YRR+h/fCxjlemG94D0p3Awbt+vn2zi/0HmvvXCQs+LMX+i3Az4AM36J84A/OuUfPuMrToACXhpJbUMLNLyxlQ+FBbjs/hx+MySEy2L3UT1VVFexa7oV57rtepzgctGzjnTPPvtDbS9fEKyIhqyGuA48DcM6VnGFtZ0QBLg3pUHkFv3h1NdOX5jO0Sxvuu6ovqXEhfkj9RA4Vw8b3vcPtG97zLlOzCGg/wNs77zrWO4+uEeFEQsZpB7iZzarvhzjnxp5GbWdEAS6B8O/FefzytVXEtWjG/Vf1ZWiX5GCXdOaqKr1R4HLfhdx3jo8IF5vmHWbvOg46j4LmAZiWVUTqdCYB/nR9P8Q5953TqO2MKMAlUNbtKuH7LyxhS1Epd1zQlVtHZwd/4Bd/Orj7eJhvnANHDkBEM+g41AvznHGQHPArRUWaPL8fQg8VCnAJpNIjFdz1ykpeXbaDETnJ3HtlX5JjA371ZMOrPArbFnhhvn4WFK3zlid1Pn6ZWsdh0CyMTyeIhAkFuIifOOeYtiiPX81YTWLLZjxwVT8Gd24T7LIa1t4tvr3zWbB5LlSUQbOW3iH2Y4Ee3z7IRYo0TgpwET9bvWM/t7ywlLy9h/nR2K7cNLJL4zqkXpfyQ7BlnjfXee4s39SoQNtevo5w47xOcZFBnCBGpBFRgIs0gJKyo9z5n5W8uWIno7ul8Lcr+pLUKjrYZQWOc1C49niYb1vgDSITk+gNHpM9BrqcryFeRc6AAlykgTjneH7BVn77xue0iY3moWv6cU7HJnpd9eF9X75MrbTQW57aE7LP98I8c6jOnYucAgW4SANbmb+fW/61lB37DvPT8d24cURnrClfT11VBQUrvUDfMNvbO686ClExkDXMC/MuYyClm647FzmBoAe4mT0FfA3Y7ZzrVct6A+4HJgKHgG8755ae7H0V4BJK9h8+yk9fXs47qwu4oHsqf/16HxJaNqFD6idSXgpb5h8P9D253vLW7b0Z1bqcD51Ha1Q4kRpCIcBHAgeBZ+sI8Il4Y6pPBAYD9zvnBp/sfRXgEmqcczz90Rb+8NbnxMc047sjOnPtkI7ENlenri/Zt80L843vezOrle3nizHbj507zxio+c6lyQt6gPuKyALeqCPA/wF84Jyb6nu+DhjlnNt5ovdUgEuoWpm/nz+/s5Z5uUUktGzG9cM6cd3QLOJjFEhfUVnhjQS3cbYX6PmLwFVBdBx0Gnn8/HlS52BXKhJw4RDgbwB/dM7N9z2fDfzMOfeVdDazKcAUgMzMzHO2bt3akGWLnJFleft46P1c3vt8N3HNo7huaBbXD+/UtHqrn6rD+7zrzTfOhg3vw/5t3vLETtB1PHSb4I0Qp71zaQIaVYBXpz1wCRerd+znkTkbmblqJy2iIrl2SCY3juhMaqjPNx5szsGejb5z5+/Cpg+h8gg0j/fGbO82wZtZLSYh2JWKNIhwCHAdQpcmYcPuEh6Zs5HXlu8gMsK4emAHppzXhfYJMcEuLTyUl3pjta97C9a/DYeKICLK2yPvNtEL9MSsYFcp4jfhEOAXAbdyvBPbA865QSd7TwW4hKute0p59IONTF+aD8Bl/TP4/qgudGzTKsiVhZGqSti+BNbN9AK9cK23PLWHF+TdJkK7/hAREdw6Rc5A0APczKYCo4BkoAD4FdAMwDn3mO8ysoeA8XiXkX3nZIfPQQEu4W/7vsM8/uFGpi7Ko7LKMblPO24e3YXsVE3jecr2bPT2yte9BVs/9kaFa5UK3cZ7Yd7pPIhuGewqRU5J0AO8oSjApbHYfaCM/5u3iecXbKOsopKJvdK5ZXQ2Pdq1DnZp4elQsTca3LqZkPselJd4g8h0HuXtnXcdD3Ftg12lyEkpwEXCRHFpOU/N38wzH2+h5EgFF3Rvy63nZ9O3gzppnbaKctg639szX/fW8QlY2g/w9s4zh0Jab2ih/yxJ6FGAi4SZ/YeP8szHW3jqo83sO3SUETnJ3D4mh4FZGqnsjDgHBat9YT4TdlQb8DGpM6SdDelnQ3ofSOsDsSnBq1UEBbhI2Dp4pIIXFmzl/+ZtouhgOQOzErl2SEfG90qjeVRksMsLfwcLYecy320F7FwO+6qNLRHXzgv0NF+op58N8R00frsEjAJcJMwdLq9k6sJt/PPjLWwrPkRSq2i+fk4GVw/KJCtZPdf96vA+2LXSC/NdvlAvWu+NDgfedKlf7Kn39R636QIR+g+V+J8CXKSRqKpyfLSxiBcWbOPdzwuorHIMy27DNYM6cmGPtkRH6ZKpBlF+CHav+fKe+u41UFnurW/WCtJ6HQ/2lO7eIfmWSdpblzOiABdphHYfKOPfi/OYujCP7fsOkxzbnCsGeHvlHZJ0uVSDqzwKheuq7amv8O7LDx5v0zwekjp5YX7s1qaLd98qReEuJ6UAF2nEKqscc9cX8sKn23h/bQEOGJGTwjcGZzLmrFSiIrVXHjBVVbB3MxTlQvGmL9/2bfOuTT8mOvar4Z7UGZK6QFyawl0ABbhIk7Fz/2FeXJTHtIV57DpQRtvWzblyQAeuHJSp4VqDrfKoF+I1g714E+zdAlUVx9tGxfjCvFrAJ2R686e3TofmrRXwTYQCXKSJqaisYs66Qv716VY+WF+IAaO7pXLN4ExGdUslMkJ//ENKZQUcyD8e6Huqh/vm4+faj4mOhbh0aN3u+C0u/XjAt24PLZM1jGwjoAAXacLyig/x4qI8XlycR2HJEdrFt+DKgZlcObADafGaDS3kVVXCge2wPx8O7PBuJTu9ZQd2es8P7vryHjxARDNfqPuCPu5Y2PsC/th/ADQta0hTgIsIRyurmP15AS98uo15uUVERhhjzkrl2iEdGZGTjOmQbPiqqoTSwjoCfrvv+Q44eujLr4toBilnQdse0Lan79YLYtvqEH2IUICLyJds3VPK1IV5vLQ4jz2l5ZydEc/t5+cwpnuqgryxcg7K9lcL9x3eBDC713ij0x3YfrxtTNLxMG/b0wv4lO6aDCYIFOAiUqvyiipe/Ww7D83ZwLbiQ/Rq35ofjOnKBQrypudQ8fEwP3bbvabaXrt5l8C17QmpPY/vsSd01Ln2BqQAF5ETOlp5PMi37jlEz3at+cGYHC7s0VZB3pQduyzui2Bf5d0XbwZ8+REdC6ndj++xp3aHNtk6DO8nCnARqZeKyipeXbaDB9/PZeueQ/RIb83tY3IY26MtEeq5LseUl8LutccD/Vi4l+073iY6zttjb5Nd7dbFu7WID17tYUYBLiKnpKKyiteW7eChORvYXFTKWWlx3HFBDmN7pCnIpXbOeefXd3/unVvfs+H4bd82vthjB2iVWi3QfeGenAOJWRDVPFhbEJIU4CJyWioqq3h9xQ4enL2BTb4gv31MDuN7KsjlFBwt8warqR7qx0K+dPfxdhbhDVhTc689qQvEZzTJCWMU4CJyRiqrHK8v38ED7+eyqbCUbm29IJ/QS0EuZ+jwPije+NW99j0bvzyufESUN5VrYhYkdvTdZ3md6BKzvFniGuE5dwW4iPhFZZXjjRU7eGB2LhsLS+naNpbbx+QwsVe6glz8yzk4WHA80Pdu9eZq37vFux3a8+X2zVsfD/ZjoZ7YyVuWkBm2h+YV4CLiV5VVjjdX7uSB2bls2H2QnNRYbhuTw0W90zVMqwTGkRIv1Pdu+XKwHwv6irJqjc0bdS6h2p57Uiev53xy15AejU4BLiINorLKMdMX5Lm7D5KdGstt52fztbPbKcgleKqqvHPr1UP92ON9W71BbI51qouM9kajSzsb0np787q37QUxCUErv7qQCHAzGw/cD0QCTzjn/lhjfSbwDJDga3Onc27mid5TAS4SGqqqHDNXeUG+vuAgXVJacddF3Tn/rLbBLk3kqyqOeNeyF6zy5nDf5bsvLTzeJiHzeKi37eXdJ2QG/Dx70APczCKB9cCFQD6wCLjaObemWpvHgc+cc4+aWQ9gpnMu60TvqwAXCS1VVY63V+/ir7PWsamwlPPPSuUXX+tBp+RWwS5N5ORKCmDXSi/MC1Z5j4ty+WJvvXn88b30tN7eLeWsBj2/XleARzXYJ37VIGCDc26Tr6BpwGRgTbU2DmjtexwP7AhgfSLiBxERxsTe6VzQvS3PfLyF+2fnMvbeD7lheGduPT+b2OaB/LMjcori2nq3nAuOLysv9a5t37Xy+G3pc3C01FsfEQXJ3Y4HeveLvY5zDSyQe+CXA+Odc9/1Pf8mMNg5d2u1NunALCARaAVc4JxbUsvL42PRAAAU9klEQVR7TQGmAGRmZp6zdevWAGyBiJyO3SVl/Pntdby8JJ/UuOb8z8SzuKRvew3PKuHt2BCzu1Z8OdhLdsI3X4Uuo/32UaFwCL0+Af5DX01/M7NzgSeBXs65qrreV4fQRcLDZ9v2cveM1SzP30//zAR+PakXvTM0nKY0MqVF3tjwzVr47S3rCvBATh+zHehQ7XmGb1l1NwD/BnDOfQK0AJIDUp2INKh+mYm8cvMw/nz52WwrPsSkh+fzP/9ZwZ6DR4Jdmoj/tEr2a3ifSCADfBGQY2adzCwauAqYUaPNNmAMgJl1xwvwQkSkUYiIMK4Y0IH3fzyKG4Z14qXF+Yz66wc8/dFmjlbWeaBNRGoRsAB3zlUAtwLvAJ8D/3bOrTaz35jZJF+zHwE3mtlyYCrwbRfuF6qLyFe0btGM//1aD96+YwR9OyTw69fXcNED8/hoQ1GwSxMJGxrIRUSCyjnHu2sK+O2ba8grPsyEXmn8fGJ3OiS1DHZpIiEhFC4jExH5CjNjbM80RnZN4Yl5m3h4zkbeX7ubm87rwk3ndSEmuunNPiVSH4E8By4iUqcWzSK59fwcZv/oPMb2TOP+2blc8PcPmblyJ+F+pFCkISjARSSktEuI4cGr+/HilCHEtYji5heW8o0nPmXdrpJglyYSUhTgIhKSBnduwxu3Dee3l/Rizc4DTHxgHnfPWE1e8aFglyYSEtSJTURC3t7Scv7+7npe+HQrVQ76ZSYwuU87Ljq7HSlx4TnHs0h9BX0ktoaiABdpOrbvO8zry3fw2rIdfL7zABEGw7KTubhPO8b3SqN1i9Cd01nkdCnARaRRyS0oYcbyHcxYvoOtew4RHRXB6G4pTO7bnvPPSqVFM/Vel8ZBAS4ijZJzjuX5+5mxbAevr9hBYckRYptHMbZnWyb1acfw7GSiItXdR8KXAlxEGr3KKseCTXuYsWwHM1ftpKSsgjatopnYO53JfdvRPzORiAjNgibhRQEuIk3KkYpKPlxXyGvLdzD78wLKjlbRPiGGi/u0Y1KfdnRPj9OUphIWFOAi0mQdPFLBu2t2MWPZDubmFlFZ5chJjWVSn3ZM6tuOjm1aBbtEkTopwEVEgOLScmau3MmMZTtYuKUYgEGdkvjmkI6M65lGdJTOl0toUYCLiNSwfd9hXlu2nakLt5FXfJjk2OZcOTCDqwdlkpGoyVQkNCjARUTqUFXlmJtbyPMLtvH+2gIARndL5dohHRnZNYVIdXyTIFKAi4jUw/Z9h5n66TamLcqj6OAROiTFcM2gjlwxIIM2sRr1TQJPAS4icgrKK6qYtWYXzy/YyoJNxURHRjChdxrXDunIgI6J6sEuAaMAFxE5TRt2l/D8gm1MX5pPSVkF3drGce2QTC7p1544Dd8qDUwBLiJyhg6VV/D68h08t2Arq7YfoFV0JJf0a8+1QzrSPb11sMuTRkoBLiLiJ8eGb31+wVZeX76DIxVVnNMxkWuHZDKhV7rGYRe/UoCLiDSAfYfKeXlJPv/6dBubikpJbNmMKwZ0YGLvdHq2a61x2OWMKcBFRBqQc46PN+7h+QVbmbWmgMoqR8voSPpnJjIgK5FBWUn0zUygZXRUsEuVMFNXgAf0J8nMxgP3A5HAE865P9bS5grgbsABy51z1wSyRhGR02FmDMtOZlh2MoUlR1iwaQ+LtxSzcMte7p+di3MQFWH0bB/PoKxEBmQlMTAriaRW0cEuXcJUwPbAzSwSWA9cCOQDi4CrnXNrqrXJAf4NnO+c22tmqc653Sd6X+2Bi0io23/4KEu37WXR5mIWbSlmed5+yiurAMhOjWVgViIDfYGekRijS9TkS0JhD3wQsME5t8lX0DRgMrCmWpsbgYedc3sBThbeIiLhID6mGaO7pTK6WyoAZUcrWbl9Pws3F7N4SzFvrNjJ1IV5AKS1bsHATklfhHq3tnGaAlVqFcgAbw/kVXueDwyu0aYrgJl9hHeY/W7n3NuBKU9EJDBaNIv8Yo8bvHnM1xeUsGhLMQs3F7Nw8x5eX74DgLgWUQzomMjATkkM7tSGPhnx6hgnQIDPgddDFJADjAIygLlm1ts5t696IzObAkwByMzMDHSNIiJ+FRlhdE9vTff01nzr3Cycc+TvPeztoW/1Qn3OukLAC/ShXdowPCeFEdnJdGzTUofcm6hABvh2oEO15xm+ZdXlA586544Cm81sPV6gL6reyDn3OPA4eOfAG6xiEZEgMDM6JLWkQ1JLLjsnA4A9B4/w8cY9zM8tYv6GIt5Z7U26kpEYw4icZIZnpzAsuw0JLdUprqkIZCe2KLxObGPwgnsRcI1zbnW1NuPxOrZdZ2bJwGdAX+fcnrreV53YRKSpcc6xuaiU+RuKmJdbxIKNeyg5UoEZnN0+nuG+QO/fMYHmURpUJtyFxHXgZjYRuA/v/PZTzrnfm9lvgMXOuRnmHQf6GzAeqAR+75ybdqL3VICLSFNXUVnF8vx9zMstYn5uEZ/l7aOyyhHTLJLBnZMYnp3MiJwUuraN1eH2MBQSAd4QFOAiIl92oOwoCzbuYf4GL9A3FZUCkBrXnOE5yYzI8a5XT41rEeRKpT4U4CIiTdT2fYeZn1vIvNwiPtpQxN5DRwE4Ky2O889K5dL+7clOjQtylVIXBbiIiFBV5Viz8wDzcouYl1vIp5uLqaxy9MmI5/JzMri4Tzt1hAsxCnAREfmK3SVlzFi2g5eX5LN2VwnRkRGM6Z7KZf0zOK9bCs10zXnQKcBFROSEVu/Yz/Ql23lt2Xb2lJaTHBvNpD7tueyc9vRsFx/s8posBbiIiNTL0coqPlhXyPQl+cxeW8DRSsdZaXFcfk4Gk/u2JyWuebBLbFIU4CIicsr2lpbz+oodTF+Sz/L8/URGGKO6pnDZORmM6Z6q68wDQAEuIiJnZMPuEl5esp1XPsun4MAR4mOacXGfdC7rn0HfDgm6xryBKMBFRMQvKqscH20oYvrSfN5ZvYuyo1V0SWnFpf0zuLR/e9LjY4JdYqOiABcREb8rKTvKzJU7mb5kOwu3FGMGfTskkNWmFRmJMXRIbElGYgwZiS1JT2ihXu2nQQEuIiINauueUv6zdDufbNrD9r2H2bn/MFXVIibCID0+hvZfCnYv3DskxZDWuoWmSq2FAlxERALqaGUVu/aXkVd8iPy9h8nf693n+e53HSijegRFRhjp8S2+tNeekRhDh6SWdEpu1WR7v9cV4KE2H7iIiDQSzSIjvpgWtTblFVXs2Hf4i3A/Fuz5ew/z4fpCdpcc+VL7rm1jGZGTwvCcZAZ3SqJldNOOsKa99SIiEjTRURFkJbciK7lVrevLjlayY99h8vYeZu3OA8zfUMRzC7by5PzNREdGMCArkRE5KYzISaZHemsiIppWL3gdQhcRkbBRdrSSRVuKmZdbxNz1hazdVQJAm1bRDMv2ZlobkZNCWnzjmWlN58BFRKTR2X2g7ItpU+fmFlF00Dvs3pgOtyvARUSkUXPOsXZXCfN8U6cu3FzMkYqqsD/crgAXEZEm5WSH20d2TWHMWakktgrt6VMV4CIi0qTtLinjow1FzFt//HB7VIRxbpc2TOydztgebWkTG3qXqinARUREfJxzrNp+gLdW7WTmyp1s2XOICIMhndswoXc643q2JTUuNDrCKcBFRERqcezc+Vsrd/Lmyp1sLCzFDAZmJTGxVxrje6UHtVe7AlxERKQe1heUMHPlTt5auYt1Bd558wEdE5nQO50JvdJolxDYyVoU4CIiIqdow+6DvL1qJzNX7mLNzgOAN1nLxN5pTOiVXucoc/4UEgFuZuOB+4FI4Ann3B/raHcZ8DIw0Dl3wnRWgIuISCBsKSpl5ipvz3zl9v0A9G4fz4TeaUzslV7niHJnKugBbmaRwHrgQiAfWARc7ZxbU6NdHPAmEA3cqgAXEZFQk1d8yNcBbhfL8vYB0CO9NRN7p3Fp/wy/HmavK8ADOW/bIGCDc26Tc64cmAZMrqXdb4E/AWUBrE1ERKTeOiS1ZMrILrx6yzA+uvN8fvG1HrSMjuSvs9azqbA0IDUEcmy59kBetef5wODqDcysP9DBOfemmf2krjcysynAFIDMzMwGKFVERKR+2ifEcMPwTtwwvBO79peRHBuYgWFCZuZ0M4sA/g786GRtnXOPO+cGOOcGpKSkNHxxIiIi9ZAW34KoyMBEayADfDvQodrzDN+yY+KAXsAHZrYFGALMMLOvHPcXERFp6gIZ4IuAHDPrZGbRwFXAjGMrnXP7nXPJzrks51wWsACYdLJObCIiIk1RwALcOVcB3Aq8A3wO/Ns5t9rMfmNmkwJVh4iISGMQ0AlSnXMzgZk1lv2yjrajAlGTiIhIOAqZTmwiIiJSfwpwERGRMKQAFxERCUMKcBERkTCkABcREQlDYT+dqJkVAlv9+JbJQJEf3y8UaJvCQ2Pbpsa2PaBtCheNbZs6Oue+Muxo2Ae4v5nZ4tpmfQln2qbw0Ni2qbFtD2ibwkVj3Kba6BC6iIhIGFKAi4iIhCEF+Fc9HuwCGoC2KTw0tm1qbNsD2qZw0Ri36St0DlxERCQMaQ9cREQkDCnARUREwlCTDXAzG29m68xsg5ndWcv65mb2om/9p2aWFfgq68/MOpjZHDNbY2arzewHtbQZZWb7zWyZ71brTHChxMy2mNlKX71fmRvePA/4vqcVZtY/GHXWh5l1q/Zvv8zMDpjZHTXahMV3ZGZPmdluM1tVbVmSmb1rZrm++8Q6Xnudr02umV0XuKrrVsf2/MXM1vp+rl4xs4Q6XnvCn9FgqWOb7jaz7dV+vibW8doT/n0Mljq26cVq27PFzJbV8dqQ/J7OiHOuyd2ASGAj0BmIBpYDPWq0uRl4zPf4KuDFYNd9km1KB/r7HscB62vZplHAG8Gu9RS3awuQfIL1E4G3AAOGAJ8Gu+Z6blcksAtvgIaw+46AkUB/YFW1ZX8G7vQ9vhP4Uy2vSwI2+e4TfY8TQ3R7xgJRvsd/qm17fOtO+DMaYtt0N/Djk7zupH8fQ2mbaqz/G/DLcPqezuTWVPfABwEbnHObnHPlwDRgco02k4FnfI9fBsaYmQWwxlPinNvpnFvqe1wCfA60D25VATEZeNZ5FgAJZpYe7KLqYQyw0Tnnz1EEA8Y5NxcorrG4+u/MM8Altbx0HPCuc67YObcXeBcY32CF1lNt2+Ocm+Wcq/A9XQBkBLywM1DHd1Qf9fn7GBQn2ibf3+crgKkBLSqImmqAtwfyqj3P56th90Ub3y/xfqBNQKo7Q77D/f2AT2tZfa6ZLTezt8ysZ0ALOz0OmGVmS8xsSi3r6/NdhqKrqPsPTbh9R8e0dc7t9D3eBbStpU24fl/X4x3pqc3JfkZDza2+0wJP1XGaI1y/oxFAgXMut4714fY9nVRTDfBGy8xigenAHc65AzVWL8U7ZNsHeBB4NdD1nYbhzrn+wATgFjMbGeyCzpSZRQOTgJdqWR2O39FXOO+YZaO4RtXM7gIqgBfqaBJOP6OPAl2AvsBOvEPOjcXVnHjvO5y+p3ppqgG+HehQ7XmGb1mtbcwsCogH9gSkutNkZs3wwvsF59x/aq53zh1wzh30PZ4JNDOz5ACXeUqcc9t997uBV/AO71VXn+8y1EwAljrnCmquCMfvqJqCY6cvfPe7a2kTVt+XmX0b+BrwDd9/Sr6iHj+jIcM5V+Ccq3TOVQH/R+21htV3BF/8jb4UeLGuNuH0PdVXUw3wRUCOmXXy7Q1dBcyo0WYGcKyH7OXA+3X9AocC3/mfJ4HPnXN/r6NN2rHz+GY2CO/7D9n/lJhZKzOLO/YYr1PRqhrNZgDf8vVGHwLsr3YYN1TVuacQbt9RDdV/Z64DXqulzTvAWDNL9B2+HetbFnLMbDzwU2CSc+5QHW3q8zMaMmr0D/kvaq+1Pn8fQ80FwFrnXH5tK8Pte6q3YPeiC9YNr/fyerzelnf5lv0G75cVoAXeIc4NwEKgc7BrPsn2DMc7ZLkCWOa7TQRuAm7ytbkVWI3Xq3QBMDTYdZ9kmzr7al3uq/vY91R9mwx42Pc9rgQGBLvuk2xTK7xAjq+2LOy+I7z/gOwEjuKdI70Br4/IbCAXeA9I8rUdADxR7bXX+36vNgDfCfa2nGB7NuCdCz72+3TsqpR2wMwT/YyGwq2ObXrO93uyAi+U02tuk+/5V/4+hsKttm3yLf/nsd+ham3D4ns6k5uGUhUREQlDTfUQuoiISFhTgIuIiIQhBbiIiEgYUoCLiIiEIQW4iIhIGFKAi0iD882y5swsrMYTFwllCnAREZEwpAAXEREJQwpwkSbAzG4zs7VmVmZmuWZ2l2/8aMxsi5n93syeMLMDZlZkZveYWUS118eZ2T/MrNDMjpjZYjMbW+MzUs3saTMr8H3OOjO7vkYp3c1srpkdMrM1ZjYhAJsv0ihFBbsAEWlYZnY38B3gDrwhQbsDj+ENF/wLX7PbgPuAgXiTPDwGFAD3+9Y/5Vt3LbANb/jXN8zsbOfcWjOLAT4EDgPfADYB2UBSjXL+CvwMb4jOnwMvmllH580NLiKnQEOpijRiZtYSKAIudc69XW35t4AHnHMJZrYFyHPOjai2/h7gm865DmaWjTe++UXOmyHtWJulwDLn3PVmdgPemPTZrpYJJcxsFDAHuMz5Zsozs7Z484aPd86F5IQmIqFMe+AijVtPIAaYbmbV/7ceCbQwsxTf809qvO4j4H/MrDXQw7dsbo02c4FzfY/PAdbUFt41LDv2wDlXYGaVQNt6bYmIfIkCXKRxO3Ye++t4s0vVVBzAWgDKa1mmvjgip0G/OCKN22qgDG863A213Cp97YbUeN1QYLtz7oDvPQBG1mgzkuNzKi8Beug6b5HAUYCLNGLOuYPAPcA9ZnaLmXUzs55mdpWZ/ala075mdreZdTWza4AfAH/zvcdG4CXgETMbZ2Znmdn9QC/gL77XTwW2AjPM7AIz62RmY8zsykBtq0hTo0PoIo2cc+63ZrYTuBUvlA/jHU7/Z7VmDwIdgcXAUeAhjvdAB/guXlg/D7QGVgJfc86t9X3GITM7D/gzMA2IBbYAf2yo7RJp6tQLXaSJ8/VCf8I597tg1yIi9adD6CIiImFIAS4iIhKGdAhdREQkDGkPXEREJAwpwEVERMKQAlxERCQMKcBFRETCkAJcREQkDP0/QHft/OkpzRAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have trained the model we can evaluate on the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.8111\n",
      "\tacc: 0.7830\n"
     ]
    }
   ],
   "source": [
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making predictions with the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's get the predictions themselves for all nodes using another node iterator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_nodes = node_subjects.index\n",
    "all_mapper = generator.flow(all_nodes)\n",
    "all_predictions = model.predict(all_mapper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These predictions will be the output of the softmax layer, so to get final categories we'll use the `inverse_transform` method of our target attribute specification to turn these values back to the original categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "node_predictions = target_encoding.inverse_transform(all_predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at a few:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>31336</td>\n",
       "      <td>Theory</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1061127</td>\n",
       "      <td>Rule_Learning</td>\n",
       "      <td>Rule_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1106406</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13195</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "      <td>Reinforcement_Learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37879</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1126012</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "      <td>Probabilistic_Methods</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1107140</td>\n",
       "      <td>Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1102850</td>\n",
       "      <td>Theory</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31349</td>\n",
       "      <td>Theory</td>\n",
       "      <td>Neural_Networks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1106418</td>\n",
       "      <td>Theory</td>\n",
       "      <td>Theory</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Predicted                    True\n",
       "31336                    Theory         Neural_Networks\n",
       "1061127           Rule_Learning           Rule_Learning\n",
       "1106406  Reinforcement_Learning  Reinforcement_Learning\n",
       "13195    Reinforcement_Learning  Reinforcement_Learning\n",
       "37879     Probabilistic_Methods   Probabilistic_Methods\n",
       "1126012   Probabilistic_Methods   Probabilistic_Methods\n",
       "1107140                  Theory                  Theory\n",
       "1102850                  Theory         Neural_Networks\n",
       "31349                    Theory         Neural_Networks\n",
       "1106418                  Theory                  Theory"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"Predicted\": node_predictions, \"True\": node_subjects})\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node embeddings\n",
    "Evaluate node embeddings as activations of the output of GraphSAGE layer stack, and visualise them, coloring nodes by their subject label.\n",
    "\n",
    "The GraphSAGE embeddings are the output of the GraphSAGE layers, namely the `x_out` variable. Let's create a new model with the same inputs as we used previously `x_inp` but now the output is the embeddings rather than the predicted class. Additionally note that the weights trained previously are kept in the new model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "embedding_model = Model(inputs=x_inp, outputs=x_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708, 32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = embedding_model.predict(all_mapper)\n",
    "emb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Project the embeddings to 2d using either TSNE or PCA transform, and visualise, coloring nodes by their subject label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.manifold import TSNE\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = emb\n",
    "y = np.argmax(target_encoding.transform(node_subjects), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "if X.shape[1] > 2:\n",
    "    transform = TSNE  # PCA\n",
    "\n",
    "    trans = transform(n_components=2)\n",
    "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_subjects.index)\n",
    "    emb_transformed[\"label\"] = y\n",
    "else:\n",
    "    emb_transformed = pd.DataFrame(X, index=node_subjects.index)\n",
    "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
    "    emb_transformed[\"label\"] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0],\n",
    "    emb_transformed[1],\n",
    "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
    "    cmap=\"jet\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that each class of paper is separated into three general regions. This is due to the fact that the GraphSAGE features from the previous layer for the node itself, the aggregated in-neighbours, and the aggregated out-neighbours are currently concatenated in the form `[node, in_agg, out_agg]`.\n",
    "\n",
    "There are four distinct types of directed neighbourhoods, namely: \n",
    "* Both in-neighbours and out-neighbours;\n",
    "* Only in-neighbours – in this case `out_agg` will be zero; \n",
    "* Only out-neighbours – in this case `in_agg` will be zero;\n",
    "* No in-neighbours or out-neighbours – in this case both `in_agg` and `out_agg` will be zero.\n",
    "\n",
    "The fourth case, isolated nodes having no in-neighbours and no out-neighbours, does not occur for the CORA dataset (see the counts below) therefore the CORA dataset consists of the three types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 nodes have no in or out neighbours\n",
      "486 nodes have in but not out neighbours\n",
      "1143 nodes have out but no in neighbours\n",
      "1079 nodes have in and out neighbours\n"
     ]
    }
   ],
   "source": [
    "directed_neigh_type = [\n",
    "    1 * (len(G.in_nodes(node)) > 0) + 2 * (len(G.out_nodes(node)) > 0)\n",
    "    for node in node_subjects.index\n",
    "]\n",
    "\n",
    "print(f\"{sum(nt==0 for nt in directed_neigh_type)} nodes have no in or out neighbours\")\n",
    "print(f\"{sum(nt==1 for nt in directed_neigh_type)} nodes have in but not out neighbours\")\n",
    "print(f\"{sum(nt==2 for nt in directed_neigh_type)} nodes have out but no in neighbours\")\n",
    "print(f\"{sum(nt==3 for nt in directed_neigh_type)} nodes have in and out neighbours\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We visualize the clustering of these different directed neighbourhood types below:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "alpha = 0.7\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7, 7))\n",
    "ax.scatter(\n",
    "    emb_transformed[0], emb_transformed[1], c=directed_neigh_type, cmap=\"jet\", alpha=alpha\n",
    ")\n",
    "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
    "plt.title(\n",
    "    \"{} visualization of GraphSAGE embeddings for cora dataset\".format(transform.__name__)\n",
    ")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that we can reduce the splitting effect of these different directed neighbourhood types by adding the aggregated in-neighbourhood and out-neighbourhood rather that concatenating them. This feature is planned to be implemented soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/node-classification/directed-graphsage-node-classification.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/node-classification/directed-graphsage-node-classification.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
